Machine-to-Machine Transportation Systems

Machine-to-machine communication offers us many potential benefits. If machines are aware of each other they can work together as a system, providing additional benefits to everyone. In particular, we explored this concept for cooperative autonomous driving. By sharing data between vehicles, we were able to enhance positioning systems to the sub-one meter accuracy we envisioned would be necessary for autonomous highway driving. This would make autonomous driving possible in a platoon system with only a radio, vastly reducing the cost. We also explored how cars might talk intelligently to their drivers and to smart cities. For example, how and when should you hand off control from an autonomous vehicle to a human driver? What information does a driver or car need to know about the road ahead to make smart routing decisions? In this talk, I will explore a range of possibilities and their associated technical challenges.

About the Speaker

Jennifer Healey is a Research Scientist at Intel Corporation in Santa Clara, California. Dr. Healey is part of the Systems and Software Research Lab. She received her B.S., M.S., and Ph.D. from MIT in the Department of Electrical Engineering and Computer Science in 2000. She is an expert in embedded sensor technologies and intelligent sensor fusion and has done extensive research on situation and context aware systems. In 2010, she founded a portfolio of research around transportation that includes research around M2M (machine-to-machine) Autonomous Driving and Driver State Monitoring. Dr. Healey's work on data driven semi/autonomous control has additionally interested her in how and why data gets used in smart systems, and how user’s privacy is protected in a world where our actions constantly logged by our interaction with smart things.

Artistic Image Contest

As part of the Visionaries in Technology event, engineering students were invited to submit artistic images for a slideshow: